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Record W2124461447 · doi:10.5194/angeo-23-1071-2005

<i>Letter to the Editior</i>Testing the hypothesis of the influence of neutral turbulence on the deduction of ambipolar diffusivities from meteor trail expansion

2005· article· en· W2124461447 on OpenAlexafffund
Christopher Hall, T. Asô, Masaki Tsutsumi, Satonori Nozawa, A. H. Manson, C. E. Meek

Bibliographic record

VenueAnnales Geophysicae · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Polar ResearchNorges ForskningsrådMinistry of Education, Science and TechnologyUniversity of Saskatchewan
KeywordsMeteor (satellite)ThermosphereAmbipolar diffusionRadarMesospherePhysicsAtmospheric sciencesThermal diffusivityAltitude (triangle)TurbulenceMeteoroidComputational physicsGeophysicsIonosphereGeologyMeteorologyPlasmaStratosphereThermodynamicsMathematicsAstronomyNuclear physicsGeometry

Abstract

fetched live from OpenAlex

Abstract. Fading times of radar echoes from underdense meteor trails in the upper mesosphere/lower thermosphere are commonly used to determine ambipolar diffusivities and hence ambient temperature. Diffusivities are generally expected to increase exponentially with height through the region from which the meteor trail echoes are obtained, viz., typically 70-110km altitude for a ~30-MHz radar. In practice, however, this is more the exception: unexpectedly large diffusivities are obtained in the lower part of the regime, and unexpectedly low values are obtained in the upper part; only in the few kilometres on either side of the maximum in echo occurrence (viz., 90km for a 30-MHz radar) does the diffusivity profile behave as expected. Hall (2002) hypothesised that neutral turbulence might be enhancing expansion of the meteor trail in the lower part of the regime. In this communication, due to results only available since the publication of Hall's suggestion, we are able to refute the hypothesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.216
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2005
Admission routes2
Has abstractyes

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